Materials with different compositions exhibit slight, but measurable, differences in their absorption of near-infrared radiation. Thus, near-infrared analysis can be used to estimate the chemical composition and corresponding physical properties of materials as described in the patent application "METHOD FOR IMPROVING INFRARED ANALYSIS ESTIMATIONS BY AUTOMATICALLY COMPENSATING FOR INSTRUMENT INSTABILITIES" by DiFoggio, et al (U.S. Ser. No. 07/917,486) incorporated herein by reference.
Near-infrared (NIR) analysis is a secondary (indirect) analytical technique that is calibrated against primary (direct) analytical techniques (primary reference methods). NIR analysis requires a calibration or training set of samples of the material for which both the near-infrared spectra and primary reference measurements of the properties of interest are obtained.
Using regression mathematics, the NIR spectra of the calibration set is correlated to the primary reference measurements of the properties of these samples. The resulting regression models allow the estimation of the properties of unknown samples of material (ones for which primary reference measurements have not been made) from their NIR spectra.
NIR analysis has been applied in the petrochemical industry for analysis of both chemical composition (aromatic and saturates content) and physical properties (octane number, density, vapor pressure) of hydrocarbons including gasoline.
Octane-number ratings are a measure of the resistance of a gasoline to engine knock. There are two basic types of octane-number ratings corresponding to the conditions under which the engine test is performed. For the same gasoline, the less severe test (Research Octane Number or RON) produces higher octane-number ratings than does the more severe test (Motor Octane Number or MON). The average of RON and MON is called road octane number or (RON+MON)/2 because it represents an average performance of a gasoline under conditions of varying severity as would occur when actually driving on a road. This average also is called Pump Octane Number (PON) because it is the number which is usually posted at gas station pumps.
Generally, it is easier to use NIR to estimate chemical composition than to estimate physical properties of samples because NIR mainly measures the absorbance of vibrational modes whose number and types are determined by the chemical composition. NiR is able to estimate physical properties because the physical properties are related to the chemical composition in complex, and generally unknown, ways.
The regression models obtained from NIR analysis often lack generality. A calibration model which works well on a calibration set of samples may not work as well on unknowns. Additionally, it may not be possible to obtain a universal model that provides a satisfactory fit for all samples in calibration sets that consist of very diverse samples. Results of recent experimentation indicate that one reason for not being able to obtain a universal model is due to a fallacy in the underlying assumptions of the NIR technique itself.
One fundamental assumption of near-infrared and mid-infrared analysis is that all information needed to estimate a property of interest for a sample is contained within the infrared spectrum of that sample. If this assumption was correct, the development of a correlation model would be a straight-forward mathematical exercise of finding the most appropriate model or series of models for extracting the information contained in the spectrum.
Current work and published literature in this field are based on this fundamental assumption being valid for infrared analysis of unleaded gasolines. Failures in a model's ability to estimate unknown samples are ascribed to such things as using too small a calibration set, using a non-representative calibration set, the need to use more mathematics in manipulating the data, or incorrect values reported by the primary reference method. Poor estimations of octane number have not been attributed by the prior art to an absence of essential information in the infrared spectrum itself.
To test the fundamental NIR assumption, the spectra of 13,700 gasoline samples collected on-line at a refinery were used to develop a calibration model for octane number. Even with a calibration set of this magnitude, however, the model did not hold up over time.
Gas chromatography, infrared (IR), Raman and other analysis then were performed on gasoline samples for which NIR data also was available. The research indicated that the fundamental assumption noted above was flawed and that near and mid-infrared spectra have some information voids or "blind spots". For example, compounds that exist in trace amounts in a gasoline often have so small an effect on the spectrum that the effect is lost in the instrument noise and the interferences from a myriad of other compounds.
The use of gas chromatography (GC) as a stand-alone technique to estimate octane numbers has been described in the art for over twenty years. The prior art indicates that it takes a long time to generate a gas chromatogram and that even if all the components in a gasoline are known, the gasoline's octane number cannot accurately be determined by simple linear modeling because the octane number is not quite equal to the volume fraction of each component multiplied by its octane blending value because of interaction terms (the degree to which one component influences the effective octane number of another component) and other non-linear effects.
Hirschfeld suggested the use of NIR as a stand-alone technique to estimate octane number in 1984 in an article titled "Near-Infrared Reflectance Spectrometry: Tip of the Iceberg" (Analytical Chemistry, Vol 56, No. 8, July 1984, pp 933A-934A) that discussed applications of the 1100-2500 nm region of the near-infrared spectrum.
In early 1989, Kelly, et al of the Center for Process Analytical Chemistry (CPAC) at the University of Washington reported in "Prediction of Octane Numbers from Near-Infrared Spectral Features in the Range 660-1215 nm" (Analytical Chemistry, Vol. 61, No. 4, Feb. 15, 1989, pp 313-320) that octane numbers could be correlated to near-informed spectral features in the 3rd-overtone region (850-1050 nm). Kelly used a set of 43 unleaded California summer gasolines.
In late 1989, Maggard applied for a patent on using the 2nd-overtone region (1100-1250 nm) of the near-infrared for octane-number estimation (U.S. Pat. No. 4,963,745). Using his own training set of 90 gasoline samples (whose origin and degree of diversity are not specified), Maggard compared his own correlation to PON (R=0.9941 and SEC=0.497) using the three 3rd-overtone wavelengths (896, 932 and 1032 nm suggested in the Kelly paper) to his own correlation to PON (R=0.9887 and SEC=0.414) that was based on a single 2nd-overtone (1220 nm) wavelength. Although Maggard concluded that his invention, with only its single wavelength, provided better accuracy than Kelly's multiple correlation, it is shown below that Maggard's conclusions are not universally applicable but rather depend on the specific training set used.
It is widely known and accepted that trace amounts (on the order of 1%) of compounds cannot be accurately quantified using near-infrared spectroscopy. These trace amounts of compounds, however, can have a substantial effect on the octane number despite having little effect on the infrared spectra. For example, gasoline components such as normal decane (pure component RON rating of -57, and typical RON blending value of -33 octane numbers) can have a high impact on the octane number even when present in trace amounts: 1% of decane reduces the research octane number of a typical gasoline by 0.33.
Although it is generally accepted that NIR cannot accurately predict the octane number of leaded gasoline because the small amounts of tetraethyl lead that are used have a large impact on the octane number without significantly changing the NIR spectrum, the dramatic impact on an estimated octane number caused by the NIR missing other important trace compounds, such as the pure hydrocarbons nonane and decane, has been overlooked or ignored by the prior art. The effects of these missed trace compounds are the problem addressed by the present invention.